Tootfinder

Interactive Text-to-SQL via Expected Information Gain for Disambiguation
Luyu Qiu, Jianing Li, Chi Su, Lei Chen
https://arxiv.org/abs/2507.06467 https://…

Interactive Text-to-SQL via Expected Information Gain for Disambiguation
Relational databases are foundational to numerous domains, including business intelligence, scientific research, and enterprise systems. However, accessing and analyzing structured data often requires proficiency in SQL, which is a skill that many end users lack. With the development of Natural Language Processing (NLP) technology, the Text-to-SQL systems attempt to bridge this gap by translating natural language questions into executable SQL queries via an automated algorithm. Yet, when operat…

VQA support to Arabic Language Learning Educational Tool
Khaled Bachir Delassi (LIM Lab, Amar Telidji University, Laghouat, Algeria), Lakhdar Zeggane (LIM Lab, Amar Telidji University, Laghouat, Algeria), Hadda Cherroun (LIM Lab, Amar Telidji University, Laghouat, Algeria), Abdelhamid Haouhat (LIM Lab, Amar Telidji University, Laghouat, Algeria), Kaoutar Bouzouad (Computer Science Dept., USTHB, Algiers, Algeria)

VQA support to Arabic Language Learning Educational Tool
We address the problem of scarcity of educational Arabic Language Learning tools that advocate modern pedagogical models such as active learning which ensures language proficiency. In fact, we investigate the design and evaluation of an AI-powered educational tool designed to enhance Arabic language learning for non-native speakers with beginner-to-intermediate proficiency level. The tool leverages advanced AI models to generate interactive visual quizzes, deploying Visual Question Answering as…

How trust networks shape students' opinions about the proficiency of artificially intelligent assistants
Yutong Bu, Andrew Melatos, Robin Evans
https://arxiv.org/abs/2506.19655

How trust networks shape students' opinions about the proficiency of artificially intelligent assistants
The rising use of educational tools controlled by artificial intelligence (AI) has provoked a debate about their proficiency. While intrinsic proficiency, especially in tasks such as grading, has been measured and studied extensively, perceived proficiency remains underexplored. Here it is shown through Monte Carlo multi-agent simulations that trust networks among students influence their perceptions of the proficiency of an AI tool. A probabilistic opinion dynamics model is constructed, in whi…

ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark
He Wang, Linhan Ma, Dake Guo, Xiong Wang, Lei Xie, Jin Xu, Junyang Lin
https://arxiv.org/abs/2507.05727

ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark
Automatic Speech Recognition (ASR) has been extensively investigated, yet prior evaluative efforts have largely been restricted to contextless paradigms. This constraint stems from the limited proficiency of conventional ASR models in context modeling and their deficiency in memory and reasoning based on world knowledge. Recent breakthroughs in the development of Large Language Models (LLMs) and corresponding Large Audio Language Models (LALMs) have markedly enhanced the visibility of general a…

ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools
Shaofeng Yin, Ting Lei, Yang Liu
https://arxiv.org/abs/2508.03284 https://arxiv.org/pdf…

ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools
Integrating external tools into Large Foundation Models (LFMs) has emerged as a promising approach to enhance their problem-solving capabilities. While existing studies have demonstrated strong performance in tool-augmented Visual Question Answering (VQA), recent benchmarks reveal significant gaps in real-world tool-use proficiency, particularly in functionally diverse multimodal settings requiring multi-step reasoning. In this work, we introduce ToolVQA, a large-scale multimodal dataset compri…

What Drives Team Success? Large-Scale Evidence on the Role of the Team Player Effect
Nico Elbert, Alicia von Schenk, Fabian Kosse, Victor Klockmann, Nikolai Stein, Christoph Flath
https://arxiv.org/abs/2506.04475

What Drives Team Success? Large-Scale Evidence on the Role of the Team Player Effect
Effective teamwork is essential in structured, performance-driven environments, from professional organizations to high-stakes competitive settings. As tasks grow more complex, achieving high performance requires not only technical proficiency but also strong interpersonal skills that allow individuals to coordinate effectively within teams. While prior research has identified social skills and familiarity as key drivers of team success, their joint effects -- particularly in temporary teams --…

The AI Language Proficiency Monitor -- Tracking the Progress of LLMs on Multilingual Benchmarks
David Pomerenke, Jonas Nothnagel, Simon Ostermann
https://arxiv.org/abs/2507.08538 …

The AI Language Proficiency Monitor -- Tracking the Progress of LLMs on Multilingual Benchmarks
To ensure equitable access to the benefits of large language models (LLMs), it is essential to evaluate their capabilities across the world's languages. We introduce the AI Language Proficiency Monitor, a comprehensive multilingual benchmark that systematically assesses LLM performance across up to 200 languages, with a particular focus on low-resource languages. Our benchmark aggregates diverse tasks including translation, question answering, math, and reasoning, using datasets such as FLORES+…

# Philosophical test fails ChatGPT: AI coherence isn’t enough to prove human mind
The research reveals that #ChatGPT does exhibit proficiency in basic coherence building. It maintains consistent dictional and intentional lines by reusing phrases and aligning responses with contextual topics. It also demonstrates some ability to construct rational coherence by offering logically consistent replies…

This https://arxiv.org/abs/2311.00388 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems
Sequential recommender systems (SRS) have gained increasing popularity due to their remarkable proficiency in capturing dynamic user preferences. In the current setup of SRS, a common configuration is to uniformly consider each historical behavior as a positive interaction. However, this setting has the potential to yield sub-optimal performance as each individual item often have a different impact on shaping the user's interests. Hence, in this paper, we propose a novel automatic sampling fram…

PAC Bench: Do Foundation Models Understand Prerequisites for Executing Manipulation Policies?
Atharva Gundawar, Som Sagar, Ransalu Senanayake
https://arxiv.org/abs/2506.23725

PAC Bench: Do Foundation Models Understand Prerequisites for Executing Manipulation Policies?
Vision-Language Models (VLMs) are increasingly pivotal for generalist robot manipulation, enabling tasks such as physical reasoning, policy generation, and failure detection. However, their proficiency in these high-level applications often assumes a deep understanding of low-level physical prerequisites, a capability that remains largely unverified. For robots to perform actions reliably, they must comprehend intrinsic object properties (e.g., material, weight), action affordances (e.g., grasp…

On the analysis of sequential designs without a specified number of observations
Anna Klimova, Tam\'as Rudas
https://arxiv.org/abs/2507.02580 https://

On the analysis of sequential designs without a specified number of observations
The paper focuses on sequential experiments for categorical responses in which whether or not a further observation is made depends on the outcome of a previous experiment. Examples include subsequent medical interventions being performed or not depending on the result of a previous intervention, data about offsprings, life tables, and repeated educational retraininig until a certain proficiency level is achieved. Such experiments do not lead to data with a full Cartesian product structure and,…

WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks
Atsuyuki Miyai, Zaiying Zhao, Kazuki Egashira, Atsuki Sato, Tatsumi Sunada, Shota Onohara, Hiromasa Yamanishi, Mashiro Toyooka, Kunato Nishina, Ryoma Maeda, Kiyoharu Aizawa, Toshihiko Yamasaki
https://arxiv.org/abs/2506.01952

WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks
Powered by a large language model (LLM), a web browsing agent operates web browsers in a human-like manner and offers a highly transparent path toward automating a wide range of everyday tasks. As web agents become increasingly capable and demonstrate proficiency in general browsing tasks, a critical question emerges: Can they go beyond general browsing to robustly handle tasks that are tedious and complex, or chores that humans often avoid doing themselves? In this paper, we introduce WebChore…

This https://arxiv.org/abs/2502.00698 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models
IQ testing has served as a foundational methodology for evaluating human cognitive capabilities, deliberately decoupling assessment from linguistic background, language proficiency, or domain-specific knowledge to isolate core competencies in abstraction and reasoning. Yet, artificial intelligence research currently lacks systematic benchmarks to quantify these critical cognitive capabilities in multimodal systems. To address this crucial gap, we propose MM-IQ, a comprehensive evaluation framew…

Optimizing Mastery Learning by Fast-Forwarding Over-Practice Steps
Meng Xia, Robin Schmucker, Conrad Borchers, Vincent Aleven
https://arxiv.org/abs/2506.17577

Optimizing Mastery Learning by Fast-Forwarding Over-Practice Steps
Mastery learning improves learning proficiency and efficiency. However, the overpractice of skills--students spending time on skills they have already mastered--remains a fundamental challenge for tutoring systems. Previous research has reduced overpractice through the development of better problem selection algorithms and the authoring of focused practice tasks. However, few efforts have concentrated on reducing overpractice through step-level adaptivity, which can avoid resource-intensive cur…

Enhancing Complex Injection Mold Design Validation Using Multicombined RV Environments
J. M. Mercado-Colmenero, D. F. Garcia-Molina, B. Gutierrez-Jimenez, C. Martin-Donate
https://arxiv.org/abs/2507.20732

Enhancing Complex Injection Mold Design Validation Using Multicombined RV Environments
The intricate design of real complex injection molds poses significant challenges. Mold design vali-dation often falls to operators with tool-handling experience but limited CAD proficiency. Unlike other industries, the scale and costs of injection mold fabrication hinder prototyping before pro-duction. Virtual reality (VR) has emerged as a revolutionary solution offering a safe, immersive, and realistic experience and accessible using QR codes. This paper presents a new multimodal virtual envi…

How Do Code Smells Affect Skill Growth in Scratch Novice Programmers?
Ricardo Hidalgo Arag\'on, Jes\'us M. Gonz\'alez-Barahona, Gregorio Robles
https://arxiv.org/abs/2507.17314

How Do Code Smells Affect Skill Growth in Scratch Novice Programmers?
Context. Code smells, which are recurring anomalies in design or style, have been extensively researched in professional code. However, their significance in block-based projects created by novices is still largely unknown. Block-based environments such as Scratch offer a unique, data-rich setting to examine how emergent design problems intersect with the cultivation of computational-thinking (CT) skills. Objective. This research explores the connection between CT proficiency and design-level c…

My guide to the CEFR levels of language proficiency

Investigating Hallucination in Conversations for Low Resource Languages
Amit Das, Md. Najib Hasan, Souvika Sarkar, Zheng Zhang, Fatemeh Jamshidi, Tathagata Bhattacharya, Nilanjana Raychawdhury, Dongji Feng, Vinija Jain, Aman Chadha
https://arxiv.org/abs/2507.22720

Investigating Hallucination in Conversations for Low Resource Languages
Large Language Models (LLMs) have demonstrated remarkable proficiency in generating text that closely resemble human writing. However, they often generate factually incorrect statements, a problem typically referred to as 'hallucination'. Addressing hallucination is crucial for enhancing the reliability and effectiveness of LLMs. While much research has focused on hallucinations in English, our study extends this investigation to conversational data in three languages: Hindi, Farsi, and Mandari…

Ball path curvature and in-game free throw shooting proficiency in the National Basketball Association
Ruoqian Zhu, Dave Love, Scott Powers
https://arxiv.org/abs/2506.13779

Ball path curvature and in-game free throw shooting proficiency in the National Basketball Association
Basketball shooting coaches agree that smoother shooting motions are better, but there is less agreement about what "smooth" means quantitatively or what part of the shooting motion needs to be smooth. Using ball tracking data from the 2023-2024 National Basketball Association regular season, we explore the relationship between ball path curvature and free throw shooting performance. We fit Bézier curves to the ball tracking data in the sagittal plane and test different methods of calculating …

StreamLink: Large-Language-Model Driven Distributed Data Engineering System
Dawei Feng, Di Mei, Huiri Tan, Lei Ren, Xianying Lou, Zhangxi Tan
https://arxiv.org/abs/2505.21575

StreamLink: Large-Language-Model Driven Distributed Data Engineering System
Large Language Models (LLMs) have shown remarkable proficiency in natural language understanding (NLU), opening doors for innovative applications. We introduce StreamLink - an LLM-driven distributed data system designed to improve the efficiency and accessibility of data engineering tasks. We build StreamLink on top of distributed frameworks such as Apache Spark and Hadoop to handle large data at scale. One of the important design philosophies of StreamLink is to respect user data privacy by ut…

The Missing Link: Joint Legal Citation Prediction using Heterogeneous Graph Enrichment
Lorenz Wendlinger, Simon Alexander Nonn, Abdullah Al Zubaer, Michael Granitzer
https://arxiv.org/abs/2506.22165

The Missing Link: Joint Legal Citation Prediction using Heterogeneous Graph Enrichment
Legal systems heavily rely on cross-citations of legal norms as well as previous court decisions. Practitioners, novices and legal AI systems need access to these relevant data to inform appraisals and judgments. We propose a Graph-Neural-Network (GNN) link prediction model that can identify Case-Law and Case-Case citations with high proficiency through fusion of semantic and topological information. We introduce adapted relational graph convolutions operating on an extended and enriched versio…

Prospects for sub-GeV astrophysical neutrino detection with IceCube
Per Arne Sevle Myhr (for the IceCube Collaboration), Gwenha\"el de Wasseige (for the IceCube Collaboration)
https://arxiv.org/abs/2507.08569

Prospects for sub-GeV astrophysical neutrino detection with IceCube
The IceCube Neutrino Observatory is currently the largest and most sensitive detector for astrophysical neutrinos and has pioneered the field of high-energy neutrino astronomy. Despite being designed with the primary goal of identifying astrophysical TeV neutrinos and their corresponding sources, recent studies, utilising the DeepCore subdetector, have shown IceCube's proficiency in being sensitive to astrophysical neutrinos at GeV energies. Currently, there is a gap in sensitivity between the …

SWE-Perf: Can Language Models Optimize Code Performance on Real-World Repositories?
Xinyi He, Qian Liu, Mingzhe Du, Lin Yan, Zhijie Fan, Yiming Huang, Zejian Yuan, Zejun Ma
https://arxiv.org/abs/2507.12415

SWE-Perf: Can Language Models Optimize Code Performance on Real-World Repositories?
Code performance optimization is paramount in real-world software engineering and critical for production-level systems. While Large Language Models (LLMs) have demonstrated impressive capabilities in code generation and bug fixing, their proficiency in enhancing code performance at the repository level remains largely unexplored. To address this gap, we introduce SWE-Perf, the first benchmark specifically designed to systematically evaluate LLMs on code performance optimization tasks within au…